Visualizing Changes In Opioid Treatment Policy

J Montgomery Maxwell

import pandas as pd
import numpy as np
import gen3
import openpyxl
import plotly.express as px
import requests
import json

import plotly.io as pio
pio.renderers.default = 'notebook'

Define Data Mappings

Dictionary for mapping the names of States and Territories to their respective abbreviations. Create additional mappings for cleaning data.

state_abbrev = {
    "Alabama": "AL", "Alaska": "AK", "Arizona": "AZ", "Arkansas": "AR", "California": "CA", "Colorado": "CO", 
    "Connecticut": "CT", "Delaware": "DE", "Florida": "FL", "Georgia": "GA", "Hawaii": "HI", "Idaho": "ID",
    "Illinois": "IL", "Indiana": "IN", "Iowa": "IA", "Kansas": "KS", "Kentucky": "KY", "Louisiana": "LA",
    "Maine": "ME", "Maryland": "MD", "Massachusetts": "MA", "Michigan": "MI", "Minnesota": "MN", 
    "Mississippi": "MS", "Missouri": "MO", "Montana": "MT", "Nebraska": "NE", "Nevada": "NV",
    "New Hampshire": "NH", "New Jersey": "NJ", "New Mexico": "NM", "New York": "NY", "North Carolina": "NC", 
    "North Dakota": "ND", "Ohio": "OH",  "Oklahoma": "OK", "Oregon": "OR", "Pennsylvania": "PA", 
    "Rhode Island": "RI", "South Carolina": "SC", "South Dakota": "SD", "Tennessee": "TN", "Texas": "TX", 
    "Utah": "UT", "Vermont": "VT", "Virginia": "VA", "Washington": "WA", "West Virginia": "WV", 
    "Wisconsin": "WI", "Wyoming": "WY", "District of Columbia": "DC", "American Samoa": "AS", "Guam": "GU", 
    "Northern Mariana Islands": "MP", "Puerto Rico": "PR", "United States Minor Outlying Islands": "UM", 
    "U.S. Virgin Islands": "VI"}

policyMap = {0: 'Not Approved', 1:'Approved'}
waiverMap = {'.': 0, 0:0, 1:1}

Import And Clean Data

Import data using the Gen3 Python SDK. Importing three data files and their respective codebooks.

!gen3 drs-pull object dg.6VTS/5200158e-e9fe-44ef-96c9-e89ecd402fc4
!gen3 drs-pull object dg.6VTS/2b83e419-8d3d-4569-b9a1-a52ecd387cba
!gen3 drs-pull object dg.6VTS/a0a8785a-8663-47b9-95ea-a1813612a2f1
!gen3 drs-pull object dg.6VTS/abe9cd49-fc86-4c9b-b9d0-f8c0280d8aaa
!gen3 drs-pull object dg.6VTS/b7974ffe-2e46-47cf-9d57-4d8900d7a40f
!gen3 drs-pull object dg.6VTS/dca15d95-aac5-4879-88cb-3a740398f26c

Here we look at three key features for U.S. States. The code name for the policy is listed at the end of each line.

• Has the state approved telehealth MOUD treatment with buprenorphine or methadone for existing opioid treatment patients? (Telehealth Treatment)
• Has the state received an approved section 1115 Medicaid waiver that explicitely address SUD treatment? (SUD Medicaid Waiver)
• Have state correctional facilities approved modified policies explicitly related to MOUD treatment during COVID-19? (Incarceration MOUD Treatment)

df1 = pd.read_excel('data/jcoin-pdaps/buprenorphine-and-methadone-during-covid-19-data-020222.xlsx')
df2 = pd.read_excel('data/jcoin-pdaps/covid-19-state-medicaid-waivers-data-020222.xlsx')
df3 = pd.read_excel('data/jcoin-pdaps/covid-19-moud-at-state-correctional-facilities-data-020222.xlsx')

df1['Policy Change'] = ((df1['Telehealth_existing_bup'] + df1['Telehealth_existing_methadone']) != 0).astype(int)
df1['Policy'] = 'Telehealth MOUD Treatment'
df1 = df1[['Jurisdictions', 'Policy Change', 'Policy']]

df2['Policy Change'] = df2['JM_15sud'].map(waiverMap)
df2['Policy'] = 'SUD Medicaid Waiver'
df2 = df2[['Jurisdictions', 'Policy Change', 'Policy']]

df3['Policy Change'] = df3['JC_MOUDlaw']
df3['Policy'] = 'MOUD Treatment While Incarcerated'
df3 = df3[['Jurisdictions', 'Policy Change', 'Policy']]

df = pd.concat([df1, df2, df3],ignore_index=True)
df['Abbreviations'] = df['Jurisdictions'].map(state_abbrev)
df['Change In Policy'] = df['Policy Change'].map(policyMap)
df = df[['Jurisdictions', 'Abbreviations', 'Change In Policy', 'Policy']]
df

	Jurisdictions	Abbreviations	Change In Policy	Policy
0	Alabama	AL	Not Approved	Telehealth MOUD Treatment
1	Alaska	AK	Not Approved	Telehealth MOUD Treatment
2	Arizona	AZ	Not Approved	Telehealth MOUD Treatment
3	Arkansas	AR	Not Approved	Telehealth MOUD Treatment
4	California	CA	Not Approved	Telehealth MOUD Treatment
...	...	...	...	...
148	Virginia	VA	Approved	MOUD Treatment While Incarcerated
149	Washington	WA	Not Approved	MOUD Treatment While Incarcerated
150	West Virginia	WV	Not Approved	MOUD Treatment While Incarcerated
151	Wisconsin	WI	Not Approved	MOUD Treatment While Incarcerated
152	Wyoming	WY	Not Approved	MOUD Treatment While Incarcerated

153 rows × 4 columns

Single & Multi Feature Choropleth Maps

You can create choropleth maps using the plotly.express.choropleth package.

Both discrete and continuous data can be represented in these plots.

fig = px.choropleth(df[df['Policy'] == 'Telehealth MOUD Treatment'], locations='Abbreviations', locationmode="USA-states", 
                    color='Change In Policy', color_discrete_map={'Not Approved':'Gray', 'Approved':'Purple'}, 
                    scope='usa', title='Telehealth MOUD Treatment With Buprenorphine Or Methadone')
fig.show()

You can show multiple features or the same feature over a period of time using the 'animation_frame' argument. Here we create an interactive plot which allows us to toggle between the three changes in public health policy which we are investigating.

fig = px.choropleth(df, locations='Abbreviations', locationmode="USA-states", color='Change In Policy', 
                    color_discrete_map={'Not Approved':'Gray', 'Approved':'Purple'}, animation_frame='Policy', 
                    scope='usa', title='Changes In Opioid Treatment Policy During COVID-19')
fig.show()